1. Field of the Invention
The present invention relates to a real time multi-object tracking method, and more particularly, to a real time multi-object tracking apparatus and method using a global motion capable of improving the accuracy of multi-object tracking in tracking motions of multiple objects by applying global motion information of the multiple objects to a real time multi-object tracking.
2. Discussion of Related Art
Existing object tracking technologies are implemented through an online learning-based method in which a plurality of low performance learning machines are used in combination with each other (a related article is titled ‘Visual tracking with online multiple instance learning, CVPR 2009’) and an object tracking method in which a correlation filter is used (a related article is titled ‘High-speed tracking with kernelized correlation filters, IEEE Trans on Pattern Analysis and Machine Intelligence 2014’).
Existing object tracking technologies are mainly focused on research conducted on single object tracking, and thus have attained outstanding results in single object tracking.
In other words, since existing object tracking technology tracks objects using a method of detecting edge points of an object, when the existing objet tracking technology is applied to multi-object tracking, only multiple objects having a constant orientation are able to be tracked.
In addition, existing object tracking technology, which uses a method of detecting edge points of an object, is not suitable for tracking multiple objects in real time when the multiple objects include a small object, objects having significantly similar shapes as if uniform, an object in a sporting event image which has a dynamic and rapid motion.
In addition, among conventional object tracking technologies, deep learning-based technologies, such as scene recognition technology (a related article is titled ‘ImageNet classification with deep convolutional neural networks, NIPS 2012’), and face recognition technology (a related article is titled ‘Deepface: closing the gap to human-level performance in face verification, CVPR 2014’) produce performance similar to the recognition ability of a human for a stationary image, but have problems with being applicable to real time sporting event images containing frequent occlusions and similar appearances, and with difficulty in obtaining learning data and insufficiency in speed/recognition performance.
Accordingly, there is a need for a method of tracking multiple objects in sporting events, capable of resolving the above discussed limitations and operating in real time.
To this end, first, a background subtraction method which is robust to various conditions in a stadium (illumination change, noise, and the like) and an object tracking method using object recognition which is robust to occlusion and similar appearances and based on a detected foreground region are needed, and global motion also needs to be considered to overcome the limitations of existing technologies.